Neuronal migration during embryonic development contributes to functional brain circuitry. Many neurons migrate in morphologically distinct stages that coincide with differentiation, requiring tight spatial regulation. It had been proposed that neurotransmitter-mediated activity could exert this control. Here, we demonstrate that intracellular calcium transients occur in cerebellar neurons of zebrafish embryos during migration. We show that depolarization increases and hyperpolarization reduces the speed of tegmental hindbrain neurons using optogenetic tools and advanced track analysis optimized for in vivo migration. Finally, we introduce a compound screening assay to identify acetylcholine (ACh), glutamate, and glycine as regulators of migration, which act regionally along the neurons’ route. We summarize our findings in a model describing how different neurotransmitters spatially interact to control neuronal migration. The high evolutionary conservation of the cerebellum and hindbrain makes it likely that polarization state-driven motility constitutes an important principle in building a functional brain.

Abstract:The strategic European paper "Flightpath 2050" claims dramatic reductions of noise for aviation transport scenarios in 2050: ". . . The perceived noise emission of flying aircraft is reduced by 65%. These are relative to the capabilities of typical new aircraft in 2000. . . ". There is a consensus among experts that these far reaching objectives cannot be accomplished by application of noise reduction technologies at the level of aircraft components only. Comparably drastic claims simultaneously expressed in Flightpath 2050 for carbon dioxide and NOX reduction underline the need for step changes in aircraft technologies and aircraft configurations. New aircraft concepts with entirely different propulsion concepts will emerge, including unconventional power supplies from renewable energy sources, ranging from electric over hybrid to synthetic fuels. Given this foreseen revolution in aircraft technology the question arises, how the noise impact of these new aircraft may be assessed. Within the present contribution, a multi-level, multi-fidelity approach is proposed which enables aircraft noise assessment. It is composed by coupling noise prediction methods at three different levels of detail. On the first level, high fidelity methods for predicting the aeroacoustic behavior of aircraft components (and installations) are required since in the early stages of the development of innovative noise reduction technology test data is not available. The results are transferred to the second level, where radiation patterns of entire conventional and future aircraft concepts are assembled and noise emissions for single aircraft are computed. In the third level, large scale scenarios with many aircraft are considered to accurately predict the noise exposure for receivers on the ground. It is shown that reasonable predictions of the ground noise exposure level may be obtained. Furthermore, even though simplifications and omissions are introduced, it is shown that the method is capable of transferring all relevant physical aspects through the levels.

The application of porous materials is a common measure for noise mitigation and in room acoustics. The prediction of the acoustic behavior applies material models, among which most are based on the Biot-parameters. Thereby, it is expected that, if more Biot-parameters are used, a better prediction can be obtained. Nevertheless, an estimation of the Biot-parameters from the geometric design of the material is possible for simple structures only. For common porous materials, the microstructure is typically unknown and characterized by homogenized quantities. This contribution introduces a methodology that enables the design and optimization of porous materials based on the Biot-parameters and connects these to microscopic geometric quantities. Therefore, artificial porous materials were manufactured using 3D-printing technology with a prescribed geometric design and the influence of different design variables was investigated. The Biot-parameters were identified with an inverse procedure and it can be shown that different Biot-parameters can be influenced by adjusting the geometric design variables. Based on these findings, a one-parameter optimization procedure of the material is set up to maximize the absorption characteristics in the frequency range of interest.

Neuronal migration during development is necessary to form an ordered and functional brain. Postmitotic neurons require microtubules and dynein to move, but the mechanisms by which they contribute to migration are not fully characterized. Using tegmental hindbrain nuclei neurons in zebrafish embryos together with subcellular imaging, optogenetics, and photopharmacology, we show that, in vivo, the centrosome’s position relative to the nucleus is not linked to greatest motility in this cell type. Nevertheless, microtubules, dynein, and kinesin-1 are essential for migration, and we find that interference with endosome formation or the Golgi apparatus impairs migration to a similar extent as disrupting microtubules. In addition, an imbalance in the traffic of the model cargo Cadherin-2 also reduces neuronal migration. These results lead us to propose that microtubules act as cargo carriers to control spatiotemporal protein distribution, which in turn controls motility. This adds crucial insights into the variety of ways that microtubules can support successful neuronal migration in vivo.

Additive manufacturing (AM), widely known as 3D-printing, builds parts by adding material in a layer-by-layer process. This tool-less procedure enables the manufacturing of porous sound absorbers with defined geometric features, however, the connection of the acoustic behavior and the material’s micro-scale structure is only known for special cases. To bridge this gap, the work presented here employs machine-learning techniques that compute acoustic material parameters (Biot parameters) from the material’s micro-scale geometry. For this purpose, a set of test specimens is used that have been developed in earlier studies. The test specimens resemble generic absorbers by a regular lattice structure based on a bar design and allow a variety of parameter variations, such as bar width, or bar height. A set of 50 test specimens is manufactured by material extrusion (MEX) with a nozzle diameter of 0.2 and a targeted under extrusion to represent finer structures. For the training of the machine learning models, the Biot parameters are inversely identified from the manufactured specimen. Therefore, laboratory measurements of the flow resistivity and absorption coefficient are used. The resulting data is used for training two different machine learning models, an artificial neural network and a k-nearest neighbor approach. It can be shown that both models are able to predict the Biot parameters from the specimen’s micro-scale with reasonable accuracy. Moreover, the detour via the Biot parameters allows the application of the process for application cases that lie beyond the scope of the initial database, for example, the material behavior for other sound fields or frequency ranges can be predicted. This makes the process particularly useful for material design and takes a step forward in the direction of tailoring materials specific to their application.